Recurrent Neural Network and Multi-Factor Feature Filtering for Ransomware Detection in Android Apps

Inam Ullah Khan ()
Additional contact information
Inam Ullah Khan: Department of Computer Science,Qurtuba University of Science & Information, Technology, Peshawar, Pakistan

International Journal of Innovations in Science & Technology, 2025, vol. 6, issue 3, 1021-1030

Abstract: The market is flooded with Android Software (apps), and at the same time that number is growing quickly, and so are the many security exploits that take advantage of such apps. The effectiveness of traditional defensive systems is at risk due to the growing diversity of Android malware.This situation has sparked significant interest in improving malware detection accuracy and scalability for smart devices. By examining the Long Short-Term Memory (LSTM) method, we have developed an effective deep learning-based malware detection modelfor enhanced Android ransomware detection. For feature selection, eight different methods were applied. By comparing the outcomes of all feature selection procedures, we used a simple majority vote process to choose the 19 crucial characteristics. The Android Malware dataset (CI-CAndMal2017) and common performance metrics were used to assess the proposed technique. With a detection accuracy of 97.08%, our model surpasses existing approaches. We advocate our proposed method as effective in malware and forensic analysis based on its remarkable performance.

Keywords: Ransomware Malware; Recurrent Neural Network(RNN); Long Short-Term Memory(LSTM); AndriodSecurity (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.50sea.com/index.php/IJIST/article/view/915/1481 (application/pdf)
https://journal.50sea.com/index.php/IJIST/article/view/915 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:6:y:2024:i:3:p:1021-1030

Access Statistics for this article

International Journal of Innovations in Science & Technology is currently edited by Prof. Dr. Syed Amer Mahmood

More articles in International Journal of Innovations in Science & Technology from 50sea
Bibliographic data for series maintained by Iqra Nazeer ().